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Corneal Temperature Reversal After Storage in Chen Medium Compared With Optisol GS

2001· article· en· W2316000415 on OpenAlexaff
Clarence Yap, Agnes Wong, Joel Naor, David S. Rootman

Bibliographic record

VenueCornea · 2001
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOphthalmologyChenComputer scienceMedicineBiology

Abstract

fetched live from OpenAlex

PURPOSE: To compare corneal endothelial cell function by measuring corneal thickness during temperature reversal between corneas stored in two different storage media, Optisol GS and Chen Medium (CM). METHODS: Twenty paired corneas from 10 human donors were randomly assigned for storage at 4 degrees C in Optisol GS (10 corneas) or CM (10 corneas). The storage media were masked, and measurements were done in a masked fashion. After storage for 48 hours, corneal thickness was measured by ultrasonic pachymetry at 2-hour intervals for 12 hours, during which time the corneas were perfused with BSS (balanced salt solution) Plus at 37 degrees C. Scanning electron microscopy of two pairs of corneas from two donors was performed to assess ultrastructural change after 12 hours of warming. RESULTS: Corneal thickness decreased during the first 4 hours of the warming period and then increased during the 6-to 12-hour warming period. These changes in corneal thickness over time were similar for the two storage media (p = 0.212). Scanning electron microscopy showed greater amounts of endothelial cell disruption in Optisol GS-stored corneas than those stored in CM after 12 hours of warming and perfusion. CONCLUSIONS: The endothelial pump of corneas stored in CM appear to be as well-preserved as those stored in Optisol GS, although greater endothelial disruption may be present with Optisol GS by scanning electron microscopy. Further studies are required to compare the clinical effectiveness of these two media.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.234
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2001
Admission routes1
Has abstractyes

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